CBE-LSE Bibliometric Analysis
Caleb Trujillo, Darcie Nelson, Rachel Kudlacz, Germaine Ng
2022-10-21
Introduction
There has been tremendous growth in the area of undergraduate biology research over the last fifteen years. This study attempts to summarize and analyze the progress of this growth for the journal of CBE-LSE.
Summary of initial data set
txt <- c("rawdata/CBE-LSE-2008-June2022/CBE-LSE-2008-June2022.txt")
data <- convert2df(file = txt, dbsource = 'wos', format = "plaintext") ##
## Converting your wos collection into a bibliographic dataframe
##
##
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
##
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)
##
##
## Missing fields: DE
## Done!
##
##
## Generating affiliation field tag AU_UN from C1: Done!results <- biblioAnalysis(data, sep = ";")
theme_default <- theme(
plot.caption = element_text(hjust = 0, face= "italic"),
plot.title.position = "plot",
plot.caption.position = "plot",
panel.background = element_rect(fill = "white"),
panel.grid = element_line(size = .25, color = "#99CCCC"),
axis.ticks = element_blank(),
legend.key = element_rect(color = "#F2F2F2"),
legend.background = element_rect(fill = "white"),
legend.position = "top",
plot.background = element_rect(fill = "white")
)After the results are processed, they are stored as data tables for information reporting.
options(width=100)
S <- summary(object = results, pause = FALSE)##
##
## MAIN INFORMATION ABOUT DATA
##
## Timespan 2008 : 2022
## Sources (Journals, Books, etc) 1
## Documents 1072
## Annual Growth Rate % -1.83
## Document Average Age 6.54
## Average citations per doc 20.3
## Average citations per year per doc 2.577
## References 26262
##
## DOCUMENT TYPES
## article 920
## bibliography 1
## book review 1
## correction 22
## editorial material 90
## letter 33
## review 5
##
## DOCUMENT CONTENTS
## Keywords Plus (ID) 1302
## Author's Keywords (DE) 0
##
## AUTHORS
## Authors 3187
## Author Appearances 4746
## Authors of single-authored docs 93
##
## AUTHORS COLLABORATION
## Single-authored docs 157
## Documents per Author 0.336
## Co-Authors per Doc 4.43
## International co-authorships % 4.571
##
##
## Annual Scientific Production
##
## Year Articles
## 2008 57
## 2009 43
## 2010 73
## 2011 45
## 2012 53
## 2013 82
## 2014 78
## 2015 60
## 2016 109
## 2017 89
## 2018 94
## 2019 85
## 2020 86
## 2021 74
## 2022 44
##
## Annual Percentage Growth Rate -1.832022
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 BROWNELL SE 36 TANNER KD 14.79
## 2 TANNER KD 34 DOLAN EL 12.34
## 3 DOLAN EL 28 ALLEN D 12.31
## 4 KNIGHT JK 26 EDDY SL 11.17
## 5 EDDY SL 21 STARK LA 10.29
## 6 SMITH MK 21 DOLAN E 9.00
## 7 COOPER KM 19 BROWNELL SE 8.57
## 8 ALLEN D 15 HOOPES LLM 7.03
## 9 HOOPES LLM 15 KNIGHT JK 6.94
## 10 STARK LA 15 GOUVEA JS 5.33
##
##
## Top manuscripts per citations
##
## Paper DOI TC TCperYear NTC
## 1 AUCHINCLOSS LC, 2014, 10.1187/cbe.14-01-0004 444 49.3 11.06
## 2 JENSEN JL, 2015, 10.1187/cbe.14-08-0129 335 41.9 8.41
## 3 CROWE A, 2008, 10.1187/cbe.08-05-0024 308 20.5 12.67
## 4 ARMBRUSTER P, 2009, 10.1187/cbe.09-03-0025 285 20.4 13.80
## 5 BROWNELL SE, 2012, 10.1187/cbe.12-09-0163 269 24.5 8.40
## 6 BANGERA G, 2014, 10.1187/cbe.14-06-0099 265 29.4 6.60
## 7 EDDY SL, 2014, 10.1187/cbe.14-03-0050 245 27.2 6.11
## 8 MAHER JM, 2013, 10.1187/cbe.13-04-0082 219 21.9 8.50
## 9 SMITH MK, 2013, 10.1187/cbe.13-08-0154 216 21.6 8.39
## 10 TANNER KD, 2012, 10.1187/cbe.12-03-0033 214 19.5 6.68
##
##
## Corresponding Author's Countries
##
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 979 0.93238 947 32 0.0327
## 2 CANADA 23 0.02190 19 4 0.1739
## 3 GERMANY 6 0.00571 5 1 0.1667
## 4 SWEDEN 6 0.00571 2 4 0.6667
## 5 AUSTRALIA 5 0.00476 4 1 0.2000
## 6 NETHERLANDS 5 0.00476 5 0 0.0000
## 7 ISRAEL 3 0.00286 2 1 0.3333
## 8 BRAZIL 2 0.00190 2 0 0.0000
## 9 FRANCE 2 0.00190 1 1 0.5000
## 10 NEW ZEALAND 2 0.00190 1 1 0.5000
##
##
## SCP: Single Country Publications
##
## MCP: Multiple Country Publications
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 USA 20738 21.18
## 2 CANADA 290 12.61
## 3 SWEDEN 192 32.00
## 4 NETHERLANDS 172 34.40
## 5 AUSTRALIA 98 19.60
## 6 CZECH REPUBLIC 41 41.00
## 7 NORWAY 33 16.50
## 8 ISRAEL 31 10.33
## 9 GERMANY 28 4.67
## 10 CHINA 21 21.00
##
##
## Most Relevant Sources
##
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 1072S## $MainInformation
## [1] "\n\nMAIN INFORMATION ABOUT DATA\n\n" "Timespan 2008 : 2022 \n"
## [3] "Sources (Journals, Books, etc) 1 \n" "Documents 1072 \n"
## [5] "Annual Growth Rate % -1.83 \n" "Document Average Age 6.54 \n"
## [7] "Average citations per doc 20.3 \n" "Average citations per year per doc 2.577 \n"
## [9] "References 26262 \n" "\nDOCUMENT TYPES \n"
## [11] "article 920 \n" "bibliography 1 \n"
## [13] "book review 1 \n" "correction 22 \n"
## [15] "editorial material 90 \n" "letter 33 \n"
## [17] "review 5 \n" "\nDOCUMENT CONTENTS\n"
## [19] "Keywords Plus (ID) 1302 \n" "Author's Keywords (DE) 0 \n"
## [21] "\nAUTHORS\n" "Authors 3187 \n"
## [23] "Author Appearances 4746 \n" "Authors of single-authored docs 93 \n"
## [25] "\nAUTHORS COLLABORATION\n" "Single-authored docs 157 \n"
## [27] "Documents per Author 0.336 \n" "Co-Authors per Doc 4.43 \n"
## [29] "International co-authorships % 4.571 \n" "\n"
##
## $MainInformationDF
## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2008:2022
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 1072
## 5 Annual Growth Rate % - 1.83
## 6 Document Average Age 6.54
## 7 Average citations per doc 20.3
## 8 Average citations per year per doc 2.577
## 9 References 26262
## 10 DOCUMENT TYPES
## 11 article 920
## 12 bibliography 1
## 13 book review 1
## 14 correction 22
## 15 editorial material 90
## 16 letter 33
## 17 review 5
## 18 DOCUMENT CONTENTS
## 19 Keywords Plus (ID) 1302
## 20 Author's Keywords (DE) 0
## 21 AUTHORS
## 22 Authors 3187
## 23 Author Appearances 4746
## 24 Authors of single-authored docs 93
## 25 AUTHORS COLLABORATION
## 26 Single-authored docs 157
## 27 Documents per Author 0.336
## 28 Co-Authors per Doc 4.43
## 29 International co-authorships % 4.571
## 30
##
## $AnnualProduction
## Year Articles
## 1 2008 57
## 2 2009 43
## 3 2010 73
## 4 2011 45
## 5 2012 53
## 6 2013 82
## 7 2014 78
## 8 2015 60
## 9 2016 109
## 10 2017 89
## 11 2018 94
## 12 2019 85
## 13 2020 86
## 14 2021 74
## 15 2022 44
##
## $AnnualGrowthRate
## [1] -1.832022
##
## $MostProdAuthors
## Authors Articles Authors Articles Fractionalized
## 1 BROWNELL SE 36 TANNER KD 14.79
## 2 TANNER KD 34 DOLAN EL 12.34
## 3 DOLAN EL 28 ALLEN D 12.31
## 4 KNIGHT JK 26 EDDY SL 11.17
## 5 EDDY SL 21 STARK LA 10.29
## 6 SMITH MK 21 DOLAN E 9.00
## 7 COOPER KM 19 BROWNELL SE 8.57
## 8 ALLEN D 15 HOOPES LLM 7.03
## 9 HOOPES LLM 15 KNIGHT JK 6.94
## 10 STARK LA 15 GOUVEA JS 5.33
##
## $MostCitedPapers
## Paper DOI TC TCperYear NTC
## 1 AUCHINCLOSS LC, 2014, 10.1187/cbe.14-01-0004 444 49.3 11.06
## 2 JENSEN JL, 2015, 10.1187/cbe.14-08-0129 335 41.9 8.41
## 3 CROWE A, 2008, 10.1187/cbe.08-05-0024 308 20.5 12.67
## 4 ARMBRUSTER P, 2009, 10.1187/cbe.09-03-0025 285 20.4 13.80
## 5 BROWNELL SE, 2012, 10.1187/cbe.12-09-0163 269 24.5 8.40
## 6 BANGERA G, 2014, 10.1187/cbe.14-06-0099 265 29.4 6.60
## 7 EDDY SL, 2014, 10.1187/cbe.14-03-0050 245 27.2 6.11
## 8 MAHER JM, 2013, 10.1187/cbe.13-04-0082 219 21.9 8.50
## 9 SMITH MK, 2013, 10.1187/cbe.13-08-0154 216 21.6 8.39
## 10 TANNER KD, 2012, 10.1187/cbe.12-03-0033 214 19.5 6.68
##
## $MostProdCountries
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 979 0.93238 947 32 0.0327
## 2 CANADA 23 0.02190 19 4 0.1739
## 3 GERMANY 6 0.00571 5 1 0.1667
## 4 SWEDEN 6 0.00571 2 4 0.6667
## 5 AUSTRALIA 5 0.00476 4 1 0.2000
## 6 NETHERLANDS 5 0.00476 5 0 0.0000
## 7 ISRAEL 3 0.00286 2 1 0.3333
## 8 BRAZIL 2 0.00190 2 0 0.0000
## 9 FRANCE 2 0.00190 1 1 0.5000
## 10 NEW ZEALAND 2 0.00190 1 1 0.5000
##
## $TCperCountries
## Country Total Citations Average Article Citations
## 1 USA 20738 21.18
## 2 CANADA 290 12.61
## 3 SWEDEN 192 32.00
## 4 NETHERLANDS 172 34.40
## 5 AUSTRALIA 98 19.60
## 6 CZECH REPUBLIC 41 41.00
## 7 NORWAY 33 16.50
## 8 ISRAEL 31 10.33
## 9 GERMANY 28 4.67
## 10 CHINA 21 21.00
##
## $MostRelSources
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 1072
##
## $MostRelKeywords
## NULL#plot(x=results, k=10, pause=F)
S$MainInformationDF## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2008:2022
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 1072
## 5 Annual Growth Rate % - 1.83
## 6 Document Average Age 6.54
## 7 Average citations per doc 20.3
## 8 Average citations per year per doc 2.577
## 9 References 26262
## 10 DOCUMENT TYPES
## 11 article 920
## 12 bibliography 1
## 13 book review 1
## 14 correction 22
## 15 editorial material 90
## 16 letter 33
## 17 review 5
## 18 DOCUMENT CONTENTS
## 19 Keywords Plus (ID) 1302
## 20 Author's Keywords (DE) 0
## 21 AUTHORS
## 22 Authors 3187
## 23 Author Appearances 4746
## 24 Authors of single-authored docs 93
## 25 AUTHORS COLLABORATION
## 26 Single-authored docs 157
## 27 Documents per Author 0.336
## 28 Co-Authors per Doc 4.43
## 29 International co-authorships % 4.571
## 30dir.create("tables")## Warning in dir.create("tables"): 'tables' already existswrite.csv(file = "tables/keywords.csv",as.data.frame(results$ID[1:20]))
write.csv(file = "tables/Maininformation.csv",S$MainInformationDF)
write.csv(file = "tables/ArticlesYears.csv",S$AnnualProduction)
write.csv(file = "tables/MostCitedPapers.csv",S$MostCitedPapers)
write.csv(file = "tables/MostProdAuthors.csv", S$MostProdAuthors)
write.csv(file = "tables/Universities.csv", results$Affiliations)Research Questions
Basic overview of the field
colnames(S$AnnualProduction) <- make.names(colnames(S$AnnualProduction))
ggplot(data = S$AnnualProduction, mapping = aes(x = Year..., y = Articles, group = 1)) +
geom_line(stat = "identity", color = "#266b6e", size = 1.5) +
geom_line(data = data.frame(x = as.factor(c(2021,2022)), y = c(74,40 + 25 + 20)), aes(x,y), linetype = 6, stat = "identity", color = "#1d4f60", size = 1, #alpha = 0.8
) + # added projected articles.
#theme_dark() +
scale_x_discrete(breaks = function(x){x[c(TRUE, FALSE)]}) +
theme_default +
theme(panel.grid.major.x = element_blank(),panel.grid.minor = element_blank(),panel.grid.major.y = element_line(size = 0.2))+
labs(
title = "Articles published per year",
x = NULL,
y = NULL,
)
ggsave("figures/AnnualProductivity.jpg",
device = "jpeg", dpi = 400, bg = "white",
width = 7, height = 4, units = "in", limitsize = FALSE)Authoring
S$MostProdAuthors ## Authors Articles Authors Articles Fractionalized
## 1 BROWNELL SE 36 TANNER KD 14.79
## 2 TANNER KD 34 DOLAN EL 12.34
## 3 DOLAN EL 28 ALLEN D 12.31
## 4 KNIGHT JK 26 EDDY SL 11.17
## 5 EDDY SL 21 STARK LA 10.29
## 6 SMITH MK 21 DOLAN E 9.00
## 7 COOPER KM 19 BROWNELL SE 8.57
## 8 ALLEN D 15 HOOPES LLM 7.03
## 9 HOOPES LLM 15 KNIGHT JK 6.94
## 10 STARK LA 15 GOUVEA JS 5.33authors=gsub(","," ",names(results$Authors))
indices <- Hindex(data, field = "author", elements = authors, sep = ";", years = Inf)
indices$H %>%
dplyr::arrange(desc(NP))## Element h_index g_index m_index TC NP PY_start
## 1 BROWNELL SE 18 36 1.63636364 1423 36 2012
## 2 TANNER KD 16 34 1.06666667 1351 34 2008
## 3 DOLAN EL 14 28 1.07692308 1354 28 2010
## 4 KNIGHT JK 15 26 1.00000000 844 26 2008
## 5 EDDY SL 9 21 1.00000000 760 21 2014
## 6 SMITH MK 14 21 0.93333333 905 21 2008
## 7 COOPER KM 8 15 1.14285714 251 19 2016
## 8 ALLEN D 3 9 0.20000000 83 15 2008
## 9 HOOPES LLM 2 15 0.13333333 225 15 2008
## 10 STARK LA 4 5 0.26666667 40 15 2008
## 11 COUCH BA 10 14 1.25000000 255 14 2015
## 12 ANDREWS TC 7 13 0.77777778 222 13 2014
## 13 GRAHAM MJ 9 13 1.12500000 514 13 2015
## 14 SCHUSSLER EE 7 13 0.58333333 187 13 2011
## 15 CORWIN LA 7 12 0.87500000 443 12 2015
## 16 HANAUER DI 8 12 0.72727273 791 12 2012
## 17 WENDEROTH MP 9 12 0.60000000 872 12 2008
## 18 PRICE RM 8 11 0.72727273 181 11 2012
## 19 URBAN-LURAIN M 7 11 0.53846154 216 11 2010
## 20 BRAME CJ 6 10 0.40000000 340 10 2008
## 21 LABOV JB 6 10 0.40000000 201 10 2008
## 22 LEMONS PP 7 10 0.63636364 183 10 2012
## 23 BALLEN CJ 4 9 0.66666667 189 9 2017
## 24 BARNES ME 7 9 1.00000000 119 9 2016
## 25 COTNER S 5 9 0.35714286 133 9 2009
## 26 DOLAN E 1 1 0.06666667 3 9 2008
## 27 GIN LE 5 9 1.00000000 121 9 2018
## 28 LOPATTO D 7 9 0.53846154 556 9 2010
## 29 MOMSEN JL 5 9 0.38461538 317 9 2010
## 30 EBERT-MAY D 6 8 0.46153846 536 8 2010
## 31 FREEMAN S 8 8 0.61538462 422 8 2010
## 32 GORMALLY C 5 8 0.45454545 207 8 2012
## 33 GOUVEA JS 2 7 0.20000000 58 8 2013
## 34 NEHM RH 6 8 0.50000000 132 8 2011
## 35 ANDERSON TR 4 7 0.44444444 80 7 2014
## 36 BRICKMAN P 6 7 0.54545455 313 7 2012
## 37 CROWE AJ 6 7 0.66666667 170 7 2014
## 38 FREY RF 3 5 0.42857143 33 7 2016
## 39 MARBACH-AD G 5 7 0.38461538 165 7 2010
## 40 MCGEE R 6 7 0.85714286 274 7 2016
## 41 MERRILL J 5 7 0.45454545 130 7 2012
## 42 NEWMAN DL 4 7 0.36363636 98 7 2012
## 43 OFFERDAHL EG 5 7 0.38461538 99 7 2010
## 44 PEREZ KE 7 7 0.53846154 165 7 2010
## 45 SCHINSKE JN 5 7 0.62500000 176 7 2015
## 46 SHORTLIDGE EE 5 7 1.00000000 63 7 2018
## 47 WOOD WB 6 7 0.40000000 462 7 2008
## 48 AIKENS ML 4 6 0.57142857 102 6 2016
## 49 BECK CW 4 6 0.44444444 127 6 2014
## 50 BIROL G 6 6 0.50000000 166 6 2011
## 51 BRANCHAW J 4 6 0.30769231 92 6 2010
## 52 ELGIN SCR 6 6 0.46153846 313 6 2010
## 53 ESTRADA M 4 6 0.57142857 303 6 2016
## 54 KALINOWSKI ST 6 6 0.46153846 308 6 2010
## 55 LEONARD MJ 6 6 0.46153846 308 6 2010
## 56 PELAEZ NJ 5 6 0.41666667 66 6 2011
## 57 PREVOST LB 6 6 0.54545455 123 6 2012
## 58 SEIDEL SB 5 6 0.50000000 213 6 2013
## 59 SPETH EB 5 6 0.38461538 141 6 2010
## 60 STAINS M 5 6 0.62500000 244 6 2015
## 61 STANTON JD 5 6 0.62500000 101 6 2015
## 62 WOODIN T 4 6 0.28571429 220 6 2009
## 63 ABRAHAM JK 3 5 0.27272727 67 5 2012
## 64 ANDREWS TM 5 5 0.38461538 288 5 2010
## 65 ASAI DJ 3 5 0.30000000 29 5 2013
## 66 BOLGER MS 3 5 0.42857143 58 5 2016
## 67 BRANCHAW JL 3 5 0.33333333 463 5 2014
## 68 BYARS-WINSTON A 4 5 0.33333333 89 5 2011
## 69 CAMPBELL AM 4 5 0.36363636 85 5 2012
## 70 COLEY JD 5 5 0.45454545 190 5 2012
## 71 COOPER MM 3 5 0.30000000 85 5 2013
## 72 DOHERTY JH 4 5 0.44444444 33 5 2014
## 73 EDDY S 4 5 0.80000000 131 5 2018
## 74 GOUVEA J 1 1 0.16666667 2 5 2017
## 75 GRUNSPAN DZ 5 5 0.55555556 176 5 2014
## 76 JENSEN JL 3 5 0.25000000 409 5 2011
## 77 KNIGHT JD 3 5 0.20000000 53 5 2008
## 78 LEVIS-FITZGERALD M 3 5 0.37500000 99 5 2015
## 79 LIMERI LB 3 5 0.75000000 39 5 2019
## 80 LIU D 1 1 0.06666667 2 5 2008
## 81 LOERTSCHER J 3 5 0.33333333 68 5 2014
## 82 LONG TM 4 5 0.30769231 212 5 2010
## 83 MARSTELLER P 5 5 0.38461538 100 5 2010
## 84 MILLER S 4 5 0.26666667 90 5 2008
## 85 MUSGROVE MMC 1 2 0.33333333 6 5 2020
## 86 O'DOWD DK 4 5 0.28571429 214 5 2009
## 87 OLIMPO JT 4 5 0.57142857 93 5 2016
## 88 PELAEZ N 3 5 0.33333333 493 5 2014
## 89 PFUND C 4 5 0.30769231 120 5 2010
## 90 REINHOLZ DL 3 5 0.75000000 31 5 2019
## 91 REYNOLDS JA 5 5 0.41666667 153 5 2011
## 92 SCHUCHARDT A 2 4 0.50000000 23 5 2019
## 93 SHUSTER M 3 4 0.21428571 19 5 2009
## 94 TANNER K 5 5 0.33333333 235 5 2008
## 95 WRIGHT LK 3 5 0.33333333 75 5 2014
## 96 ZHENG Y 3 5 0.75000000 32 5 2019
## 97 BLUMER LS 2 4 0.28571429 27 4 2016
## 98 BURRUS LW 3 4 0.37500000 91 4 2015
## 99 CAMPBELL AG 4 4 0.40000000 232 4 2013
## 100 CAVANAGH AJ 3 4 0.42857143 117 4 2016
## 101 CHEN XN 3 4 0.42857143 114 4 2016
## 102 CHUNG HM 3 4 0.23076923 267 4 2010
## 103 COLLINS TW 4 4 0.57142857 75 4 2016
## 104 DAVIDESCO I 2 3 0.50000000 14 4 2019
## 105 DAVIS GK 4 4 0.40000000 55 4 2013
## 106 DENETCLAW WF 4 4 0.40000000 248 4 2013
## 107 DONOVAN DA 3 4 0.30000000 71 4 2013
## 108 DRAKE AG 3 4 1.00000000 40 4 2020
## 109 ECKDAHL TT 3 4 0.23076923 268 4 2010
## 110 ESPARZA D 3 4 0.75000000 48 4 2019
## 111 FELDON DF 3 4 0.50000000 52 4 2017
## 112 FREDERICK J 4 4 0.36363636 147 4 2012
## 113 GARDNER GE 4 4 0.57142857 61 4 2016
## 114 GARDNER SM 3 4 0.50000000 38 4 2017
## 115 GIBBS KD 3 4 0.30000000 198 4 2013
## 116 GOODWIN EC 2 4 0.40000000 22 4 2018
## 117 GRINESKI SE 4 4 0.57142857 75 4 2016
## 118 HEBERT S 3 4 0.50000000 52 4 2017
## 119 HERNANDEZ PR 3 4 0.42857143 277 4 2016
## 120 HERRERA J 2 4 0.18181818 39 4 2012
## 121 HOSKINS SG 4 4 0.33333333 205 4 2011
## 122 HOSKINSON AM 2 4 0.15384615 25 4 2010
## 123 JOHNSON D 4 4 0.30769231 329 4 2010
## 124 KLYMKOWSKY MW 3 4 0.20000000 172 4 2008
## 125 LAURSEN SL 4 4 0.36363636 621 4 2012
## 126 LO SM 3 4 0.75000000 25 4 2019
## 127 MATZ RL 2 4 0.28571429 20 4 2016
## 128 MCDANIEL MA 3 4 0.75000000 22 4 2019
## 129 MOMSEN J 3 4 0.30000000 118 4 2013
## 130 MONTPLAISIR L 4 4 0.40000000 126 4 2013
## 131 POLLOCK C 4 4 0.40000000 84 4 2013
## 132 REEVES TD 3 4 0.42857143 79 4 2016
## 133 ROSENWALD AG 4 4 0.30769231 277 4 2010
## 134 SATO BK 3 4 0.33333333 48 4 2014
## 135 SEMSAR K 4 4 0.33333333 108 4 2011
## 136 SHAFFER CD 3 4 0.23076923 267 4 2010
## 137 SMITH JI 4 4 0.30769231 110 4 2010
## 138 STETZER MR 4 4 0.44444444 98 4 2014
## 139 SUPRIYA K 1 2 0.33333333 8 4 2020
## 140 THOMPSON KV 3 4 0.23076923 88 4 2010
## 141 THOMPSON RJ 4 4 0.33333333 145 4 2011
## 142 TRUJILLO G 3 4 0.33333333 179 4 2014
## 143 UNDERWOOD SM 3 4 0.60000000 38 4 2018
## 144 WILLIAMS KS 3 4 0.25000000 61 4 2011
## 145 WILSON KJ 3 4 0.42857143 25 4 2016
## 146 WISE SB 3 4 0.30000000 92 4 2013
## 147 WOLYNIAK MJ 2 4 0.22222222 142 4 2014
## 148 WRIGHT CD 4 4 0.57142857 66 4 2016
## 149 WRIGHT R 3 4 0.21428571 43 4 2009
## 150 WYSE SA 4 4 0.30769231 184 4 2010
## 151 AGUILAR-ROCA NM 1 3 0.08333333 15 3 2011
## 152 ANDERSON CW 3 3 0.27272727 57 3 2012
## 153 ARAGON OR 3 3 0.42857143 89 3 2016
## 154 ARNESON JB 3 3 0.50000000 54 3 2017
## 155 AUERBACH AJ 3 3 0.50000000 31 3 2017
## 156 BAILEY EG 3 3 0.50000000 21 3 2017
## 157 BARNARD D 3 3 0.23076923 266 3 2010
## 158 BASS KM 2 3 0.22222222 16 3 2014
## 159 BATZLI JM 3 3 0.27272727 34 3 2012
## 160 BEAN AJ 2 3 0.40000000 30 3 2018
## 161 BHALLA S 3 3 0.23076923 266 3 2010
## 162 BISSONNETTE SA 2 3 0.33333333 19 3 2017
## 163 BROWNELL S 3 3 0.50000000 52 3 2017
## 164 BUHLER J 3 3 0.23076923 266 3 2010
## 165 BUTZ AR 2 2 0.66666667 7 3 2020
## 166 CAHILL MJ 3 3 0.42857143 24 3 2016
## 167 CARNES M 2 3 0.16666667 77 3 2011
## 168 CHAKRAVERTY D 2 3 0.25000000 69 3 2015
## 169 CHANDRASEKARAN V 3 3 0.23076923 266 3 2010
## 170 CHUDLER EH 2 3 0.22222222 10 3 2014
## 171 CREECH C 2 2 0.66666667 8 3 2020
## 172 DEANE T 3 3 0.33333333 54 3 2014
## 173 DEHAAN RL 3 3 0.21428571 123 3 2009
## 174 DENARO K 2 3 0.50000000 9 3 2019
## 175 DEPASS AL 2 3 0.40000000 30 3 2018
## 176 DEWEY J 1 1 0.50000000 2 3 2021
## 177 DIRKS C 3 3 0.20000000 436 3 2008
## 178 DOWD JE 3 3 0.37500000 32 3 2015
## 179 DRITS-ESSER D 2 3 0.22222222 16 3 2014
## 180 DU CG 3 3 0.23076923 266 3 2010
## 181 DUNBAR D 3 3 0.25000000 284 3 2011
## 182 DURHAM MF 2 3 0.28571429 76 3 2016
## 183 EATON CD 2 3 0.28571429 11 3 2016
## 184 EVANS M 3 3 0.33333333 134 3 2014
## 185 FAGBODUN S 2 2 0.66666667 8 3 2020
## 186 FESER J 3 3 0.27272727 43 3 2012
## 187 FROHLICH D 3 3 0.23076923 266 3 2010
## 188 FUSE M 2 3 0.40000000 42 3 2018
## 189 GOODMAN AL 3 3 0.23076923 266 3 2010
## 190 GOVINDAN B 2 3 0.40000000 42 3 2018
## 191 HANDELSMAN J 2 3 0.13333333 51 3 2008
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## 2783 VOGEL J 1 1 0.07142857 11 1 2009
## 2784 VOGT G 1 1 0.10000000 3 1 2013
## 2785 VON ARNIM AG 1 1 0.16666667 3 1 2017
## 2786 VORONOFF SA 1 1 0.33333333 6 1 2020
## 2787 VRANA CJ 1 1 0.09090909 2 1 2012
## 2788 VREDENBURG VT 1 1 0.20000000 25 1 2018
## 2789 VULPERHORST J 1 1 0.14285714 56 1 2016
## 2790 VUONG E 1 1 0.33333333 13 1 2020
## 2791 WACHIRA J 1 1 0.07692308 4 1 2010
## 2792 WACHSMUTH LP 1 1 0.16666667 13 1 2017
## 2793 WACKER A 1 1 0.11111111 12 1 2014
## 2794 WADE JM 1 1 0.25000000 16 1 2019
## 2795 WAGNER DJ 1 1 0.14285714 2 1 2016
## 2796 WAHLBERG SJ 1 1 0.20000000 4 1 2018
## 2797 WAKIMOTO BT 1 1 0.09090909 8 1 2012
## 2798 WALCK-SHANNON E 1 1 0.25000000 4 1 2019
## 2799 WALCOTT RL 1 1 0.20000000 5 1 2018
## 2800 WALKER DE 1 1 0.06666667 1 1 2008
## 2801 WALKER KM 1 1 0.50000000 4 1 2021
## 2802 WALKER M 1 1 0.50000000 1 1 2021
## 2803 WALL C 1 1 0.11111111 18 1 2014
## 2804 WALLACE CS 1 1 0.09090909 10 1 2012
## 2805 WALSH K 1 1 0.10000000 2 1 2013
## 2806 WALSH LL 1 1 0.50000000 6 1 2021
## 2807 WALSTON T 1 1 0.07692308 11 1 2010
## 2808 WALT DR 1 1 0.16666667 7 1 2017
## 2809 WALTER EM 1 1 0.14285714 31 1 2016
## 2810 WALTERS T 1 1 0.25000000 15 1 2019
## 2811 WANG C 1 1 0.50000000 1 1 2021
## 2812 WANG S 1 1 0.09090909 25 1 2012
## 2813 WARD JR 1 1 0.11111111 29 1 2014
## 2814 WARD RE 1 1 1.00000000 1 1 2022
## 2815 WARE VC 1 1 1.00000000 1 1 2022
## 2816 WAREHAM HT 1 1 0.11111111 1 1 2014
## 2817 WARFA ARM 1 1 0.14285714 28 1 2016
## 2818 WARING AL 1 1 0.33333333 3 1 2020
## 2819 WARKINA TD 1 1 0.50000000 4 1 2021
## 2820 WARNER DM 1 1 0.20000000 3 1 2018
## 2821 WARNER IM 1 1 0.20000000 1 1 2018
## 2822 WARNER MH 1 1 1.00000000 1 1 2022
## 2823 WASHINGTON JM 1 1 1.00000000 1 1 2022
## 2824 WASHINGTON PM 1 1 0.10000000 8 1 2013
## 2825 WATHINGTON HD 1 1 0.16666667 5 1 2017
## 2826 WATKINS ES 1 1 0.25000000 1 1 2019
## 2827 WATKINS JC 1 1 0.07692308 7 1 2010
## 2828 WATKINS JM 1 1 0.11111111 6 1 2014
## 2829 WATSON FL 1 1 0.06666667 13 1 2008
## 2830 WATTS SW 1 1 0.25000000 6 1 2019
## 2831 WAUGH AH 1 1 0.33333333 6 1 2020
## 2832 WAYMENT T 1 1 0.25000000 9 1 2019
## 2833 WEAVER GC 1 1 0.14285714 19 1 2016
## 2834 WEAVER KF 1 1 0.14285714 13 1 2016
## 2835 WEAVER PF 1 1 0.14285714 13 1 2016
## 2836 WEBBER A 1 1 0.25000000 9 1 2019
## 2837 WEBBER K 1 1 0.08333333 44 1 2011
## 2838 WEBER CF 1 1 0.14285714 1 1 2016
## 2839 WECKER L 1 1 0.33333333 9 1 2020
## 2840 WEFER SH 1 1 0.06666667 21 1 2008
## 2841 WEFES I 1 1 0.33333333 8 1 2020
## 2842 WEIGEL EG 1 1 0.33333333 3 1 2020
## 2843 WEINSTEIN SL 1 1 0.20000000 25 1 2018
## 2844 WEIR L 1 1 0.25000000 5 1 2019
## 2845 WEIR M 1 1 0.10000000 4 1 2013
## 2846 WEISS M 1 1 0.07142857 285 1 2009
## 2847 WEISSTEIN AE 1 1 0.07692308 9 1 2010
## 2848 WELCH L 1 1 0.14285714 11 1 2016
## 2849 WELCH NT 1 1 0.09090909 9 1 2012
## 2850 WELKER JD 1 1 0.20000000 6 1 2018
## 2851 WENDEROTH AP 1 1 0.08333333 153 1 2011
## 2852 WENER-FLIGNER L 1 1 0.16666667 27 1 2017
## 2853 WEREMIJEWICZ J 1 1 0.25000000 6 1 2019
## 2854 WERNECKE U 1 1 0.20000000 7 1 2018
## 2855 WESSLER SR 1 1 0.14285714 19 1 2016
## 2856 WESTER ER 1 1 0.50000000 6 1 2021
## 2857 WESTERN T 1 1 0.07142857 11 1 2009
## 2858 WESTON M 1 1 0.12500000 23 1 2015
## 2859 WESTOVER KM 1 1 1.00000000 1 1 2022
## 2860 WHITE AR 1 1 0.16666667 8 1 2017
## 2861 WHITE C 1 1 0.07692308 4 1 2010
## 2862 WHITE G 1 1 0.10000000 4 1 2013
## 2863 WHITE HB 1 1 0.07692308 9 1 2010
## 2864 WHITE I 1 1 0.33333333 10 1 2020
## 2865 WHITE JG 1 1 0.06666667 5 1 2008
## 2866 WHITE QM 1 1 0.50000000 2 1 2021
## 2867 WHITE SJ 1 1 1.00000000 1 1 2022
## 2868 WHITE-LEWIS DK 1 1 1.00000000 1 1 2022
## 2869 WHITEFLEET-SMITH JL 1 1 1.00000000 1 1 2022
## 2870 WHITTINGTON D 1 1 0.14285714 22 1 2016
## 2871 WHITWORTH K 1 1 0.20000000 9 1 2018
## 2872 WIATROS N 1 1 0.12500000 22 1 2015
## 2873 WIBERG HK 1 1 0.16666667 9 1 2017
## 2874 WICK S 1 1 0.12500000 2 1 2015
## 2875 WIDENHORN R 1 1 0.10000000 9 1 2013
## 2876 WIEBLER J 1 1 0.16666667 1 1 2017
## 2877 WILES JR 1 1 0.12500000 10 1 2015
## 2878 WILES S 1 1 0.10000000 7 1 2013
## 2879 WILEY EA 1 1 0.11111111 19 1 2014
## 2880 WILKS J 1 1 0.33333333 5 1 2020
## 2881 WILLIAMS A 1 1 0.07692308 170 1 2010
## 2882 WILLIAMS BA 1 1 0.16666667 13 1 2017
## 2883 WILLIAMS CS 1 1 0.50000000 1 1 2021
## 2884 WILLIAMS CT 1 1 0.14285714 31 1 2016
## 2885 WILLIAMS DC 1 1 1.00000000 1 1 2022
## 2886 WILLIAMS LG 1 1 0.06666667 2 1 2008
## 2887 WILLIAMS PH 1 1 0.11111111 12 1 2014
## 2888 WILLIAMS SN 1 1 0.16666667 22 1 2017
## 2889 WILLIAMS TM 1 1 0.20000000 1 1 2018
## 2890 WILLS BD 1 1 0.50000000 3 1 2021
## 2891 WILLSIE JK 1 1 0.25000000 16 1 2019
## 2892 WILMOTH L 1 1 0.10000000 7 1 2013
## 2893 WILSON A 1 1 0.33333333 2 1 2020
## 2894 WILSON AE 1 1 0.50000000 3 1 2021
## 2895 WILSON BA 1 1 0.07692308 127 1 2010
## 2896 WILSON JA 1 1 0.50000000 1 1 2021
## 2897 WILSON RE 1 1 0.08333333 11 1 2011
## 2898 WILSON-KENNEDY Z 1 1 0.20000000 1 1 2018
## 2899 WILSON-KENNEDY ZS 1 1 0.20000000 2 1 2018
## 2900 WILTBANK LB 1 1 0.25000000 5 1 2019
## 2901 WINCE T 1 1 0.14285714 25 1 2016
## 2902 WINGERT D 1 1 0.33333333 10 1 2020
## 2903 WINNIPS JC 1 1 0.12500000 83 1 2015
## 2904 WINSTEAD R 1 1 0.50000000 1 1 2021
## 2905 WITHERS GS 1 1 0.09090909 10 1 2012
## 2906 WITHY K 1 1 0.20000000 1 1 2018
## 2907 WITT ML 1 1 0.50000000 4 1 2021
## 2908 WITTEKIND E 1 1 0.50000000 2 1 2021
## 2909 WOFFORD AM 1 1 0.33333333 9 1 2020
## 2910 WOLBACH KC 1 1 0.07692308 21 1 2010
## 2911 WOLF JG 1 1 0.50000000 4 1 2021
## 2912 WOLKOW TD 1 1 0.11111111 9 1 2014
## 2913 WOLTERS CA 1 1 0.50000000 1 1 2021
## 2914 WOMACK VY 1 1 0.33333333 9 1 2020
## 2915 WOOD CV 1 1 0.33333333 9 1 2020
## 2916 WOODARD CT 1 1 0.07692308 19 1 2010
## 2917 WOODCOCK A 1 1 1.00000000 1 1 2022
## 2918 WOODHAM S 1 1 0.16666667 9 1 2017
## 2919 WORMINGTON SV 1 1 0.12500000 9 1 2015
## 2920 WORTIS H 1 1 0.11111111 10 1 2014
## 2921 WRIGHT A 1 1 0.16666667 15 1 2017
## 2922 WRIGHT M 1 1 0.50000000 1 1 2021
## 2923 WRIGHTING DM 1 1 0.50000000 1 1 2021
## 2924 WU XB 1 1 0.06666667 15 1 2008
## 2925 WUST-ACKERMANN P 1 1 0.14285714 7 1 2016
## 2926 WYATT KH 1 1 0.11111111 29 1 2014
## 2927 WYER M 1 1 0.14285714 66 1 2016
## 2928 WYNER Y 1 1 0.33333333 1 1 2020
## 2929 XAVIER J 1 1 0.14285714 1 1 2016
## 2930 XIANG L 1 1 0.20000000 3 1 2018
## 2931 XU XY 1 1 0.16666667 7 1 2017
## 2932 YAMPOLSKY L 1 1 0.08333333 6 1 2011
## 2933 YANG XM 1 1 0.16666667 7 1 2017
## 2934 YARBOROUGH MA 1 1 0.25000000 5 1 2019
## 2935 YEN JW 1 1 0.14285714 8 1 2016
## 2936 YU B 1 1 0.50000000 1 1 2021
## 2937 YUAN J 1 1 0.11111111 12 1 2014
## 2938 YUAN R 1 1 0.06666667 1 1 2008
## 2939 ZAJIC DE 1 1 0.50000000 9 1 2021
## 2940 ZAMBRANO J 1 1 0.33333333 3 1 2020
## 2941 ZEILSTRA-RYALLS JH 1 1 1.00000000 1 1 2022
## 2942 ZHANG B 1 1 0.10000000 3 1 2013
## 2943 ZHANG H 1 1 0.08333333 42 1 2011
## 2944 ZHAO FF 1 1 0.25000000 4 1 2019
## 2945 ZHAO J 1 1 0.14285714 28 1 2016
## 2946 ZHAO JQ 1 1 0.14285714 14 1 2016
## 2947 ZHI Q 1 1 0.25000000 5 1 2019
## 2948 ZIADIE MA 1 1 0.20000000 14 1 2018
## 2949 ZIEFFLER A 1 1 0.16666667 9 1 2017
## 2950 ZIEGLER B 1 1 0.11111111 17 1 2014
## 2951 ZIEGLER L 1 1 0.16666667 9 1 2017
## 2952 ZILBERSTEIN D 1 1 0.09090909 17 1 2012
## 2953 ZIMBARDI K 1 1 0.14285714 30 1 2016
## 2954 ZINK AG 1 1 0.20000000 25 1 2018
## 2955 ZUCKERMAN AL 1 1 0.50000000 1 1 2021
## 2956 ZUKSWERT JM 1 1 0.25000000 13 1 2019as_tibble(results$Authors)## # A tibble: 3,187 × 2
## AU n
## <chr> <int>
## 1 BROWNELL SE 36
## 2 TANNER KD 34
## 3 DOLAN EL 28
## 4 KNIGHT JK 26
## 5 EDDY SL 21
## 6 SMITH MK 21
## 7 COOPER KM 19
## 8 ALLEN D 15
## 9 HOOPES LLM 15
## 10 STARK LA 15
## # … with 3,177 more rowsauthordata <- dplyr::inner_join(tibble::as_tibble(results$Authors),indices$H, by = c("AU" = "Element")) %>%
filter("n" > 5) %>% #only showing authors with at least 5 publications.
mutate(AU = str_replace(
str_to_title(AU),
"[a-zA-Z]+$",
str_to_upper(
str_extract(
AU,"[a-zA-Z]+$")
)))
authorprod <- authorProdOverTime(data, k = 10, graph = TRUE)$graph +
labs(title = "Author productivity from 2008 - 2022", x = NULL, y = NULL) +
theme_minimal() +
theme(plot.title.position = "plot",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
legend.title = element_text(),
axis.text.y = element_text(margin = margin(r = -0.05, l = 0.3, unit = "in")))## `summarise()` has grouped output by 'Author'. You can override using the `.groups` argument.
authorprod
ggsave("figures/AuthorProd.jpg",
device = "jpeg", dpi = 400, bg = "white",
width = 7, height = 4.5, units = "in", limitsize = FALSE)
authordata %>%
filter(n > 8) %>%
ggplot(aes( x = NP, y = h_index)) +
geom_point(alpha = 0.6) +
geom_label_repel(aes(label = AU), size = 2, box.padding = 0.001, min.segment.length = 0.01) +
#theme_dark() +
theme_default +
labs(y = "h—index",
x = "Publications",
caption = "CBE — Life Science Education") +
coord_flip() +
ggtitle("Author productivity", subtitle = "Number of publications and h-index for LSE authors between 2010 and 2021")
authordata %>%
filter(n > 8) %>%
ggplot(aes(x = reorder(AU, (NP)), y = NP)) +
geom_col(alpha = 1) +
#geom_label_repel(aes(label = AU), size = 2, box.padding = 0.001, min.segment.length = 0.01) +
#theme_minimal()+
theme_default +
labs(y = NULL,
x = NULL,
caption = "CBE — Life Science Education between 2010 and 2021") +
coord_flip() +
theme(axis.text.y = element_text(size = 8),
panel.grid.major.y = element_blank(),
#panel.grid.major.x = element_blank(),
#panel.grid.minor.x = element_blank(),
legend.position = 'none') +
ggtitle("Author productivity", subtitle = "Number of publications by LSE authors")
# Add color for school type using the other graph to depict these.
# Going to use full join between data on author productivity and their affiliation (university or institution). Affiliations around the world
Affiliations in the US
#Affiliation Map - US
#Joining files
msidata <- read_excel("rawdata/2020eligibilitymatrix.xlsx",
skip = 10)## Warning: Coercing boolean to numeric in L129 / R129C12## Warning: Coercing boolean to numeric in R129 / R129C18## Warning: Coercing boolean to numeric in L786 / R786C12## Warning: Coercing boolean to numeric in R786 / R786C18## Warning: Coercing boolean to numeric in L845 / R845C12## Warning: Coercing boolean to numeric in R845 / R845C18## Warning: Coercing boolean to numeric in L1471 / R1471C12## Warning: Coercing boolean to numeric in R1471 / R1471C18## Warning: Coercing boolean to numeric in L1472 / R1472C12## Warning: Coercing boolean to numeric in R1472 / R1472C18## Warning: Coercing boolean to numeric in L1473 / R1473C12## Warning: Coercing boolean to numeric in R1473 / R1473C18## Warning: Coercing boolean to numeric in L1474 / R1474C12## Warning: Coercing boolean to numeric in R1474 / R1474C18## Warning: Coercing boolean to numeric in L1475 / R1475C12## Warning: Coercing boolean to numeric in R1475 / R1475C18## Warning: Coercing boolean to numeric in L1488 / R1488C12## Warning: Coercing boolean to numeric in R1488 / R1488C18## Warning: Coercing boolean to numeric in L1642 / R1642C12## Warning: Coercing boolean to numeric in R1642 / R1642C18## Warning: Coercing boolean to numeric in L1643 / R1643C12## Warning: Coercing boolean to numeric in R1643 / R1643C18## Warning: Coercing boolean to numeric in L1644 / R1644C12## Warning: Coercing boolean to numeric in R1644 / R1644C18## Warning: Coercing boolean to numeric in L1645 / R1645C12## Warning: Coercing boolean to numeric in R1645 / R1645C18## Warning: Coercing boolean to numeric in L1646 / R1646C12## Warning: Coercing boolean to numeric in R1646 / R1646C18## Warning: Coercing boolean to numeric in L1647 / R1647C12## Warning: Coercing boolean to numeric in R1647 / R1647C18## Warning: Coercing boolean to numeric in L1649 / R1649C12## Warning: Coercing boolean to numeric in R1649 / R1649C18## Warning: Expecting numeric in AZ1765 / R1765C52: got '4R'## Warning: Coercing boolean to numeric in L1906 / R1906C12## Warning: Coercing boolean to numeric in R1906 / R1906C18## Warning: Coercing boolean to numeric in L2133 / R2133C12## Warning: Coercing boolean to numeric in R2133 / R2133C18## Warning: Coercing boolean to numeric in L2134 / R2134C12## Warning: Coercing boolean to numeric in R2134 / R2134C18## Warning: Coercing boolean to numeric in L2135 / R2135C12## Warning: Coercing boolean to numeric in R2135 / R2135C18## Warning: Coercing boolean to numeric in L2136 / R2136C12## Warning: Coercing boolean to numeric in R2136 / R2136C18## Warning: Coercing boolean to numeric in L2137 / R2137C12## Warning: Coercing boolean to numeric in R2137 / R2137C18## Warning: Coercing boolean to numeric in L2198 / R2198C12## Warning: Coercing boolean to numeric in R2198 / R2198C18## Warning: Expecting numeric in AZ2341 / R2341C52: got '4R'## Warning: Coercing boolean to numeric in L2395 / R2395C12## Warning: Coercing boolean to numeric in R2395 / R2395C18## Warning: Coercing boolean to numeric in L2396 / R2396C12## Warning: Coercing boolean to numeric in R2396 / R2396C18## Warning: Coercing boolean to numeric in L2397 / R2397C12## Warning: Coercing boolean to numeric in R2397 / R2397C18## Warning: Coercing boolean to numeric in L2641 / R2641C12## Warning: Coercing boolean to numeric in R2641 / R2641C18## Warning: Expecting numeric in AZ2643 / R2643C52: got '5R'## Warning: Coercing boolean to numeric in L2696 / R2696C12## Warning: Coercing boolean to numeric in R2696 / R2696C18## Warning: Coercing boolean to numeric in L2721 / R2721C12## Warning: Coercing boolean to numeric in R2721 / R2721C18## Warning: Coercing boolean to numeric in L2868 / R2868C12## Warning: Coercing boolean to numeric in R2868 / R2868C18## Warning: Coercing boolean to numeric in L2869 / R2869C12## Warning: Coercing boolean to numeric in R2869 / R2869C18## Warning: Coercing boolean to numeric in L2870 / R2870C12## Warning: Coercing boolean to numeric in R2870 / R2870C18## Warning: Coercing boolean to numeric in L2871 / R2871C12## Warning: Coercing boolean to numeric in R2871 / R2871C18## Warning: Coercing boolean to numeric in L2872 / R2872C12## Warning: Coercing boolean to numeric in R2872 / R2872C18## Warning: Coercing boolean to numeric in L2873 / R2873C12## Warning: Coercing boolean to numeric in R2873 / R2873C18## Warning: Coercing boolean to numeric in L2874 / R2874C12## Warning: Coercing boolean to numeric in R2874 / R2874C18## Warning: Coercing boolean to numeric in L2875 / R2875C12## Warning: Coercing boolean to numeric in R2875 / R2875C18## Warning: Coercing boolean to numeric in L2876 / R2876C12## Warning: Coercing boolean to numeric in R2876 / R2876C18## Warning: Coercing boolean to numeric in L2877 / R2877C12## Warning: Coercing boolean to numeric in R2877 / R2877C18## Warning: Coercing boolean to numeric in L2878 / R2878C12## Warning: Coercing boolean to numeric in R2878 / R2878C18## Warning: Coercing boolean to numeric in L2879 / R2879C12## Warning: Coercing boolean to numeric in R2879 / R2879C18## Warning: Coercing boolean to numeric in L2880 / R2880C12## Warning: Coercing boolean to numeric in R2880 / R2880C18## Warning: Coercing boolean to numeric in L2881 / R2881C12## Warning: Coercing boolean to numeric in R2881 / R2881C18## Warning: Coercing boolean to numeric in L2882 / R2882C12## Warning: Coercing boolean to numeric in R2882 / R2882C18## Warning: Coercing boolean to numeric in L2883 / R2883C12## Warning: Coercing boolean to numeric in R2883 / R2883C18## Warning: Coercing boolean to numeric in L2884 / R2884C12## Warning: Coercing boolean to numeric in R2884 / R2884C18## Warning: Coercing boolean to numeric in L2885 / R2885C12## Warning: Coercing boolean to numeric in R2885 / R2885C18## Warning: Coercing boolean to numeric in L2886 / R2886C12## Warning: Coercing boolean to numeric in R2886 / R2886C18## Warning: Coercing boolean to numeric in L2887 / R2887C12## Warning: Coercing boolean to numeric in R2887 / R2887C18## Warning: Coercing boolean to numeric in L2888 / R2888C12## Warning: Coercing boolean to numeric in R2888 / R2888C18## Warning: Coercing boolean to numeric in L2889 / R2889C12## Warning: Coercing boolean to numeric in R2889 / R2889C18## Warning: Coercing boolean to numeric in L2890 / R2890C12## Warning: Coercing boolean to numeric in R2890 / R2890C18## Warning: Coercing boolean to numeric in L2897 / R2897C12## Warning: Coercing boolean to numeric in R2897 / R2897C18## Warning: Coercing boolean to numeric in L2899 / R2899C12## Warning: Coercing boolean to numeric in R2899 / R2899C18## Warning: Coercing boolean to numeric in L2900 / R2900C12## Warning: Coercing boolean to numeric in R2900 / R2900C18## Warning: Coercing boolean to numeric in L2901 / R2901C12## Warning: Coercing boolean to numeric in R2901 / R2901C18## Warning: Expecting numeric in AZ2902 / R2902C52: got '5R'## Warning: Expecting numeric in AZ2904 / R2904C52: got '5R'## Warning: Coercing boolean to numeric in L2911 / R2911C12## Warning: Coercing boolean to numeric in R2911 / R2911C18## Warning: Coercing boolean to numeric in L2913 / R2913C12## Warning: Coercing boolean to numeric in R2913 / R2913C18## Warning: Coercing boolean to numeric in L2914 / R2914C12## Warning: Coercing boolean to numeric in R2914 / R2914C18## Warning: Coercing boolean to numeric in L2915 / R2915C12## Warning: Coercing boolean to numeric in R2915 / R2915C18## Warning: Coercing boolean to numeric in L2916 / R2916C12## Warning: Coercing boolean to numeric in R2916 / R2916C18## Warning: Coercing boolean to numeric in L2918 / R2918C12## Warning: Coercing boolean to numeric in R2918 / R2918C18## Warning: Coercing boolean to numeric in L3083 / R3083C12## Warning: Coercing boolean to numeric in R3083 / R3083C18## Warning: Coercing boolean to numeric in L3084 / R3084C12## Warning: Coercing boolean to numeric in R3084 / R3084C18## Warning: Coercing boolean to numeric in L3085 / R3085C12## Warning: Coercing boolean to numeric in R3085 / R3085C18## Warning: Coercing boolean to numeric in L3086 / R3086C12## Warning: Coercing boolean to numeric in R3086 / R3086C18## Warning: Coercing boolean to numeric in L3087 / R3087C12## Warning: Coercing boolean to numeric in R3087 / R3087C18## Warning: Coercing boolean to numeric in L3088 / R3088C12## Warning: Coercing boolean to numeric in R3088 / R3088C18## Warning: Coercing boolean to numeric in L3182 / R3182C12## Warning: Coercing boolean to numeric in R3182 / R3182C18## Warning: Coercing boolean to numeric in L3189 / R3189C12## Warning: Coercing boolean to numeric in R3189 / R3189C18## Warning: Coercing boolean to numeric in L3190 / R3190C12## Warning: Coercing boolean to numeric in R3190 / R3190C18## Warning: Coercing boolean to numeric in L3191 / R3191C12## Warning: Coercing boolean to numeric in R3191 / R3191C18## Warning: Expecting numeric in AZ3389 / R3389C52: got '5R'affiliations <- read_excel("tables/UnivIndex.xlsx")## New names:
## • `` -> `...1`
## • `AFF` -> `AFF...2`
## • `AFF` -> `AFF...3`
## • `` -> `...7`msiuniv <- left_join(affiliations, msidata) %>%
filter(OPEID != "NA")## Joining, by = "OPEID"#Tables
msistates <- msiuniv %>%
group_by(St) %>%
summarize(n = n(), articles = sum(Freq) ) %>%
#filter(!(St %in% c("MH", "PR", "VI", "GU"))) %>%
rename(states = St, affiliations = n) %>%
group_by(affiliations, articles)
view(msistates)
msistates$state_name <- state.name[match(msistates$states,state.abb)]
us <- fortify(map_data("state"), region = "region") %>%
mutate(region = str_to_title(region))
spatial_data <- right_join(msistates,
us,
by = c("state_name" ="region"))
affiliationMap <- ggplot() +
geom_map(data = spatial_data, map = us,
aes(x = long, y = lat, map_id = state_name, group = group, fill = affiliations, label = states),
color = "black", size = 0.25) +
coord_map("albers", lat0 = 39, lat1 = 45) +
theme_map() +
theme_default +
scale_fill_viridis_c(option = "E",
direction = -1)+
labs(fill = "Author affiliations",
title = "Number of author affilitations by state",
x = NULL,
y = NULL) +
theme(plot.margin = unit(c(1,1,1,1), "cm"),
panel.background = element_blank())## Warning: Ignoring unknown aesthetics: x, y, labelggsave("figures/AffiliationUSMap.jpg", device = "jpeg", dpi = 400, width = 7,
height = 5,
units = "in", limitsize = FALSE)
centroids <- spatial_data %>%
group_by(state_name, articles) %>%
summarise(latitude = exp(mean(log(lat))), longitude = exp(mean(log(long))))## Warning in log(long): NaNs produced## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced
## Warning in log(long): NaNs produced## `summarise()` has grouped output by 'state_name'. You can override using the `.groups` argument.#Article Map
ArticlesUSMap <- ggplot() +
geom_map(data = spatial_data, map = us,
aes(x = long, y = lat, map_id = state_name, group = group, fill = articles, label = states),
color = "white", size = 0.25) +
coord_map("moll") +
theme_map() +
labs(fill = "Publications",
title = "Articles by author's state affiliation",
caption = "DC: 23, PR: 11, HI: 6, AK: 3, VI: 2, GU: 1, MH: 1.\nCounts are summarized by state/territory of each article by each author's affiliation.",
x = NULL,
y = NULL
) +
scale_fill_carto_c(palette = "BluGrn", direction = 1) +
theme(#plot.margin = unit(c(-2,0,0,0), "cm"),
panel.background = element_blank(),
legend.background = element_blank(),
legend.direction = "vertical",
legend.title=element_blank(),
legend.key.height= unit(.5, 'cm'),
legend.key.width= unit(.5, 'cm'),
plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)
#legend.position = "top"
)## Warning: Ignoring unknown aesthetics: x, y, labelArticlesUSMap
ggsave("figures/ArticlesUSMap.jpg",
device = "jpeg", dpi = 400,
width = 7, height = 4,
units = "in", limitsize = FALSE)ggarrange(AffiliationWorldMap, ArticlesUSMap, legend = "right", ncol = 1, nrow = 2, align = c("hv"))
ggsave("figures/WorldUSMap.jpg",
device = "jpeg", dpi = 400,
width = 7, height = 8,
units = "in", bg = "white", limitsize = FALSE)## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (position_stack).
## Removed 1 rows containing missing values (geom_text).## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 1 rows containing missing values (geom_text).
temp <- msiuniv %>%
mutate_if(is.numeric,as.factor)%>%
mutate_if(is.character,as.factor)%>%
select(`Institution Name`,`AANAPISI`, `AANAPISI F`,`ANNH`,`ANNH F`,`HBCU`,`HBCU Masters`, HBGI,HSI,`HSI STEM`,NASNTI, `NASNTI F` ,`PBI F`,`PBI A`,PPOHA,SIP,TCCU) %>%
pivot_longer(cols = !`Institution Name`) %>%
mutate(eligibility = if_else(value == 1 | value == 2| value == 3, "Ineligible", "NA")) %>%
mutate(eligibility = if_else(value == 4 | value == '4R' |value == 5 | value == '5R', "Eligible or potentially eligible", eligibility))%>%
mutate(eligibility = if_else(value == 6, "Current grantee", eligibility)) %>%
mutate(eligibility = if_else(value == 0, "Undetermined", eligibility)) %>%
group_by(name,eligibility)%>%
summarize(n=n())%>%
mutate(perc = round(n/sum(n),3)) %>%
mutate(titleIII = case_when(
name == "SIP" ~ TRUE,
TRUE ~ FALSE))## `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.msisummary_n <- temp%>%
select(!perc)%>%
pivot_wider(names_from = name, values_from = c(n))
view(msisummary_n)
msisummary_perc <- temp%>%
select(!n)%>%
mutate(perc = label_percent()(perc)) %>%
pivot_wider(names_from = name, values_from = c(perc))
view(msisummary_perc)
# temp %>%
# filter(eligibility == "Current grantee") %>%
# arrange(perc, "desc") %>%
# mutate(name = as.character(name)) %>%
# mutate(perc = case_when(name == "SIP" ~ 100,
# TRUE ~ perc))%>%
# mutate(name = reorder(as.factor(name), desc(perc)), order = (perc)) %>%
# pull(name)-> x_levels
temp$name<- factor(temp$name, levels = c("SIP","HBCU Masters", "TCCU","AANAPISI", "PBI A", "ANNH","PBI F","PPOHA","AANAPISI F", "HBGI","HBCU" ,"HSI STEM","HSI"))
MSIAffiliationPlot <- temp %>%
filter(#eligibility != "Ineligible" &
eligibility != "Undetermined",
#eligibility != "Ineligible",
!is.na(name)) %>%
ggplot(aes(x = name, y = perc, fill = eligibility)) +
geom_col(position = position_stack(reverse = TRUE)) +
scale_y_continuous(labels = scales::percent, limits = c(0,1)) +
coord_flip() +
facet_grid(rows = vars(titleIII), scales = "free", space = "free")+
#theme_dark() +
#scale_fill_manual(values = c("#F26E50", "#F2A679", "#F2D6B9")) +
#scale_fill_viridis(discrete = TRUE, option = "F") +
scale_fill_manual(values = c("#266b6e", "#4da284", "#c4e6c3"
)) +
#scale_fill_carto_d(palette = "BluGrn", direction = -1) +
#c4e6c3,#96d2a4,#6dbc90,#4da284,#36877a,#266b6e,#1d4f60
theme_default +
labs(
title = "US Author affiliations by Minority Serving Institution status",
y = NULL,
x = NULL
) +
theme(legend.title = element_blank(),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(margin = margin(r = -0.5, l = 0.4, unit = "cm")),
strip.text.y = element_blank()
)
MSIAffiliationPlot
ggsave("figures/MsiAffiliations.jpg",
device = "jpeg", dpi = 400,
width = 7, height = 4,
units = "in", limitsize = FALSE)
#mutate(status = if_else(value == TRUE, "Eligible or current grantee", NA, status == if_else(value == FALSE,"Not eligible or potentially eligible", NA)))
msiuniv %>%
mutate_if(is.numeric,as.factor)%>%
mutate_if(is.character,as.factor)%>%
select(`Institution Name`,`AANAPISI`, `AANAPISI F`,`ANNH`,`ANNH F`,`HBCU`,`HBCU Masters`, HBGI,HSI,`HSI STEM`,NASNTI, `NASNTI F` ,`PBI F`,`PBI A`,PPOHA,SIP,TCCU) %>%
pivot_longer(cols = !`Institution Name`) %>%
mutate(eligibility = if_else(value == 1 | value == 2| value == 3, "Ineligible", "NA")) %>%
mutate(eligibility = if_else(value == 4 | value == '4R' |value == 5 | value == '5R', "Eligible or potentially eligible", eligibility))%>%
mutate(eligibility = if_else(value == 6, "Current grantee", eligibility)) %>%
mutate(eligibility = if_else(value == 0, "Undetermined", eligibility)) %>%
group_by(eligibility, `Institution Name`) %>%
#summarise(n = n(),sum = sum(n))%>%
unique()## # A tibble: 7,904 × 4
## # Groups: eligibility, Institution Name [1,043]
## `Institution Name` name value eligibility
## <fct> <chr> <fct> <chr>
## 1 Auburn University AANAPISI 1 Ineligible
## 2 Auburn University AANAPISI F 1 Ineligible
## 3 Auburn University ANNH 1 Ineligible
## 4 Auburn University ANNH F 1 Ineligible
## 5 Auburn University HBCU 2 Ineligible
## 6 Auburn University HBCU Masters 2 Ineligible
## 7 Auburn University HBGI 2 Ineligible
## 8 Auburn University HSI 1 Ineligible
## 9 Auburn University HSI STEM 1 Ineligible
## 10 Auburn University NASNTI 1 Ineligible
## # … with 7,894 more rows #summarise(total = sum(sum))
msiuniv%>% unique()## # A tibble: 536 × 61
## ...1 AFF...2 AFF...3 Freq OPEID Issues ...7 Insti…¹ UnitID City St sector Type …² UG Core …³ Fall …⁴
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <fct> <chr> <dbl> <dbl>
## 1 65 AUBURN UNIV AUBURN… 8 0010… <NA> <NA> Auburn… 100858 Aubu… AL 1 Pub 4yr Yes 8.92e8 26920
## 2 245 BIRMINGHAM SO… BIRMIN… 2 0010… <NA> <NA> Birmin… 100937 Birm… AL 2 Pri 4yr Yes 3.66e7 1276
## 3 758 UNIV WEST ALA… UNIVER… 1 0010… <NA> <NA> Univer… 101587 Livi… AL 1 Pub 4yr Yes 6.38e7 4395
## 4 588 TUSKEGEE UNIV TUSKEG… 1 0010… <NA> <NA> Tuskeg… 102377 Tusk… AL 2 Pri 4yr Yes 1.12e8 3181
## 5 312 UNIV ALABAMA THE UN… 2 0010… <NA> <NA> The Un… 100751 Tusc… AL 1 Pub 4yr Yes 8.77e8 35344
## 6 143 UNIV ALABAMA … UNIVER… 4 0010… <NA> <NA> Univer… 100663 Birm… AL 1 Pub 4yr Yes 1.23e9 16471
## 7 7 ARIZONA STATE… ARIZON… 43 0010… Multi… <NA> Arizon… 104151 Tempe AZ 1 Pub 4yr Yes 1.74e9 47714
## 8 203 NO ARIZONA UN… NORTHE… 3 0010… <NA> <NA> Northe… 105330 Flag… AZ 1 Pub 4yr Yes 5.27e8 26699
## 9 44 UNIV ARIZONA UNIVER… 12 0010… <NA> <NA> Univer… 104179 Tucs… AZ 1 Pub 4yr Yes 1.76e9 39290
## 10 339 ARKANSAS TECH… ARKANS… 1 0010… <NA> <NA> Arkans… 106467 Russ… AR 1 Pub 4yr Yes 1.21e8 8851
## # … with 526 more rows, 45 more variables: `Core Exp/FTE` <dbl>, `Core Exp/FTE Threshold` <dbl>,
## # `Core Exp Elig?` <chr>, FT <dbl>, FTUG <dbl>, `Pell Recip` <dbl>, `Pell %` <dbl>, `Pell Threshold` <dbl>,
## # `Pell Elig?` <chr>, `Pre-Elig?` <chr>, `Eligible for New T 3/5` <chr>, `Eligible/ Current Grant` <fct>,
## # Eligible <chr>, `Native American UG Total` <dbl>, `Pacific Islander UG Total` <dbl>, `Asian UG Total` <dbl>,
## # `AANAPISI UG Total` <dbl>, `AANAPISI UG %` <dbl>, `Native American UG %` <dbl>, `Pacific Islander %` <dbl>,
## # `Hispanic FT UG Total` <dbl>, `Hispanic %` <dbl>, `Total Enroll` <dbl>, `Total Minority Enroll except Asian` <dbl>,
## # `Total Minority except Asian %` <dbl>, `Black Enroll` <dbl>, `Black %` <dbl>, AANAPISI <chr>, `AANAPISI F` <chr>, …What are the collaborative connections between institutions?
Affiliation <- results$Affiliations[1:10]
Affiliation <- as.data.frame(Affiliation) %>%
mutate(
color = case_when(
row_number() == 1 ~ "#CFB87C",
row_number() == 2 ~ "#18453B",
row_number() == 3 ~ "#C5050C",
row_number() == 4 ~ "#BA0C2F",
row_number() == 5 ~ "#4b2e83",
row_number() == 6 ~ "#8C1D40",
row_number() == 7 ~ "#231161",
row_number() == 8 ~ "#2774AE",
row_number() == 9 ~ "#7a0019",
row_number() == 10 ~ "#a51417",
## all others should be gray
TRUE ~ "#6dbc90"
))
#Author affiliations
ggplot(Affiliation,aes(x = reorder(str_to_title(AFF), Freq), y = Freq)) +
geom_col(aes(fill = color)) +
geom_col(aes(fill = "white"), alpha = 0.7) +
geom_col(aes(y =5, fill = color)) +
geom_text(
aes(label = reorder(str_to_title(AFF), Freq), y=0.13),
hjust = 0, nudge_y = 6,
size = 3.5, fontface = "bold",
fill = "white", label.size = 0, color="black"
) +
geom_text(
aes(label = Freq),
hjust = 0, nudge_y = .5,
size = 3.5, fontface = "bold"
) +
coord_flip() +
theme_default +
labs(
x = NULL,
y = NULL,
title = "Top 10 author affiliations",
)+
scale_fill_identity(guide = "none") +
scale_x_discrete(labels = NULL)+
scale_y_continuous(labels = NULL)+
theme(panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.position = 'none',
)## Warning: Ignoring unknown parameters: fill, label.size
ggsave("figures/AffiliationsTopAuthors.jpg",
device = "jpeg", dpi = 400,
width = 7, height = 4,
units = "in", limitsize = FALSE)Most cited papers and journals
## [,1]
## FREEMAN S, 2014, P NATL ACAD SCI USA, V111, P8410, DOI 10.1073/PNAS.1319030111 151
## AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, 2011, VISION CHANGE UNDERG 149
## PRESIDENT'S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGY, 2012, ENGAGE EXCEL PRODUCI 103
## HANDELSMAN J, 2004, SCIENCE, V304, P521, DOI 10.1126/SCIENCE.1096022 89
## AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, 2011, VIS CHANG UND BIOL E 86
## CROWE A, 2008, CBE-LIFE SCI EDUC, V7, P368, DOI 10.1187/CBE.08-05-0024 81
## EDDY SL, 2014, CBE-LIFE SCI EDUC, V13, P453, DOI 10.1187/CBE.14-03-0050 76
## HAAK DC, 2011, SCIENCE, V332, P1213, DOI 10.1126/SCIENCE.1204820 74
## SEYMOUR E., 1997, TALKING LEAVING WHY 72
## HAKE RR, 1998, AM J PHYS, V66, P64, DOI 10.1119/1.18809 69
## SMITH MK, 2008, CBE-LIFE SCI EDUC, V7, P422, DOI 10.1187/CBE.08-08-0045 66
## AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC, V13, P29, DOI 10.1187/CBE.14-01-0004 65
## KNIGHT JENNIFER K, 2005, CELL BIOL EDUC, V4, P298, DOI 10.1187/05-06-0082 65
## SEYMOUR E, 2004, SCI EDUC, V88, P493, DOI 10.1002/SCE.10131 64
## RUSSELL SH, 2007, SCIENCE, V316, P548, DOI 10.1126/SCIENCE.1140384 62
## FREEMAN SCOTT, 2007, CBE LIFE SCI EDUC, V6, P132, DOI 10.1187/CBE.06-09-0194 61
## HENDERSON C, 2011, J RES SCI TEACH, V48, P952, DOI 10.1002/TEA.20439 59
## LOPATTO DAVID, 2007, CBE LIFE SCI EDUC, V6, P297, DOI 10.1187/CBE.07-06-0039 57
## HUNTER AB, 2007, SCI EDUC, V91, P36, DOI 10.1002/SCE.20173 56
## BROWNELL SE, 2012, CBE-LIFE SCI EDUC, V11, P339, DOI 10.1187/CBE.12-09-0163 55## [,1]
## CBE-LIFE SCI EDUC 751
## J RES SCI TEACH 506
## SCIENCE 321
## J COLL SCI TEACH 293
## INT J SCI EDUC 266
## J CHEM EDUC 265
## SCI EDUC 265
## J EDUC PSYCHOL 218
## AM BIOL TEACH 187
## NATURE 145Multiple Correspondence Analysis
# Show how key words appear together using Multiple Correspondence Analysis
#Consider selecting only articles that have the keywords associated
CS30 <- bibliometrix::conceptualStructure(data,
field ="ID",
ngrams = 1,
method = "MCA",
minDegree= 30,
clust= "3",
k.max=5,
stemming= FALSE,
labelsize=10,
documents=10
)## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider increasing max.overlaps
CS19 <- bibliometrix::conceptualStructure(data,
field ="ID",
ngrams = 1,
method = "MCA",
minDegree= 19,
clust= "3",
k.max=5,
stemming= FALSE,
labelsize=10,
documents=10
)## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.
## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggsave("figures/CS30ConceptualStructure.png", plot = CS30$graph_terms, device = "png")## Saving 7 x 5 in imageggsave("figures/CS30ConceptualCluster.png", plot = CS30$graph_dendogram + coord_flip(), device = "png")## Saving 7 x 5 in imageggsave("figures/19ConceptualStructure.png", plot = CS19$graph_terms, device = "png")## Saving 7 x 5 in image## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlapsggsave("figures/19ConceptualCluster.png", plot = CS19$graph_dendogram + coord_flip(), device = "png")## Saving 7 x 5 in imagehypened<-results$ID[results$ID>10] %>%
row.names()%>%
str_detect("-")
spaced<- results$ID[results$ID>10] %>%
row.names()%>%
str_detect(" ")
compound<-results$ID[results$ID>10][hypened|spaced]%>%
row.names()
compound <- compound[!compound %in% "UNDERGRADUATE RESEARCH EXPERIENCES"]
TitleTerms <- termExtraction(data, "TI",stemming = TRUE, keep.terms = compound) %>%
KeywordGrowth(Tag= "TI_TM", top = 1000) %>%
pivot_longer(cols = !c('Year')) %>%
group_by(Year) %>%
mutate(sum = sum(value), perc = value / sum) %>%
filter(value > 1) %>%
filter(perc > 0.005)## Tab
## STUDENT BIOLOG UNDERGRADU SCIENC RESEARCH LEARN TEACH EDUC EXPERI
## 361 336 236 222 216 186 167 118 92
## INTRODUCTORI DEVELOP STEM ASSESS PROGRAM MODEL IMPROV PRACTIC ANALYSI
## 89 85 83 73 58 54 53 53 51
## COURS COLLEG SCIENTIF CLASSROOM EFFECT CONCEPT FACULTI
## 51 50 50 49 49 48 47## Joining, by = "Tab"SharedTerms <- TitleTerms %>%
#select(Year,name) %>%
group_by(name) %>%
summarize(total = n()) %>%
filter(total > 2)
TopTerms<- TitleTerms %>%
filter(name %in% SharedTerms$name) %>%
group_by(Year) %>%
arrange(value, .by_group = TRUE) %>%
top_n(15)## Selecting by percTitleText <- TitleTerms %>%
group_by(name) %>%
top_n(1, Year) %>%
filter(name %in% SharedTerms$name)
TitleTerms %>%
filter(name %in% TopTerms$name) %>%
ggplot(aes(Year, perc, group = name, color=name, fill=name, label = name))+
geom_line() +
geom_text_repel(aes(label = name), data = . %>% group_by(name) %>% top_n(1, Year),
size = 2,
color = "black",
arrow = arrow(length = unit(0.02, "npc")),
box.padding = 0.5
)# + ## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#scale_y_log10(labels = label_percent()) +
#theme_default +
labs(title = "Conceptual trends based on article titles")+
theme(legend.position="none")## NULLggsave("figures/TitleLineTrends.png", device = "png", dpi = 400,
width = 7, height = 7,
units = "in")## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider increasing max.overlapsTitleTerms %>%
filter(name %in% TopTerms$name) %>%
ggplot(aes(x=reorder(as.factor(str_to_sentence(name)),cumsum(value)),y = Year, group = name, color=name, fill=name, label = name, size = perc), alpha = 0.3)+
#geom_violin(alpha = 0.3)+
#geom_point() +
#geom_line(alpha = 0.8, size = 0.5)+
geom_violin(aes(scale = perc))+
geom_text(aes(label = str_to_sentence(name)), data = . %>% group_by(name) %>% top_n(-1, Year), #%>% filter(Year > 2012),
size = 2,
color = "black",
#arrow = arrow(length = unit(0.02, "npc")),
#box.padding = 0.5
nudge_x = 0,hjust = -0)+
scale_y_continuous(labels = label_date(format = "%y")) +
scale_x_discrete()+
coord_flip ()+
theme_default +
labs(title = "Conceptual trends based on article titles", y = NULL, x = NULL, )+
theme(legend.position="none",
axis.text.y = element_blank())## Warning: Ignoring unknown aesthetics: scale
ggsave("figures/TitleTrends.png", width = 4, height = 7, device = "png")